public void MultinomialPartDistribution_MaximumLikelihood_FromClassifiedSample_UseSmoothing()
{
// arrange
var sample = new ClassifiedSample <double[]>
{
{ new[] { 1.0D, 2.0D, 0.0D }, new Class("A", 0) },
{ new[] { 3.0D, 0.0D, 2.0D }, new Class("A", 0) },
{ new[] { 0.0D, 3.0D, 1.0D }, new Class("B", 1) },
{ new[] { 0.0D, 2.0D, 0.0D }, new Class("B", 1) },
{ new[] { 0.0D, 2.0D, 2.0D }, new Class("B", 1) },
};
var n = 3; // sample[i].Key.Length - the length of the word dictionary
var distr = new MultinomialPartDistribution {
N = n, UseSmoothing = true, Alpha = 2
};
// act
var res = distr.FromSample(sample);
var dA1 = res[0][0];
var dA2 = res[0][1];
var dA3 = res[0][2];
var dB1 = res[1][0];
var dB2 = res[1][1];
var dB3 = res[1][2];
// assert
Assert.AreEqual(6.0D / 14, dA1.P, EPS);
Assert.AreEqual(4.0D / 14, dA2.P, EPS);
Assert.AreEqual(4.0D / 14, dA3.P, EPS);
Assert.AreEqual(2.0D / 16, dB1.P, EPS);
Assert.AreEqual(9.0D / 16, dB2.P, EPS);
Assert.AreEqual(5.0D / 16, dB3.P, EPS);
}
public void MultinomialPartDistribution_MaximumLikelihood_FromSample()
{
// arrange
var distr = new MultinomialPartDistribution {
TotalCount = 10
};
var sample = new[] { 2.0D, 1.0D, 0.0D, 3.0D };
// act
distr.FromSample(sample);
var pars = distr.Params;
// assert
Assert.AreEqual(0.60D, pars.P, EPS);
}
public void MultinomialPartDistribution_MaximumLikelihood_FromSample_UseSmoothing()
{
// arrange
var distr = new MultinomialPartDistribution {
N = 10, UseSmoothing = true, Alpha = 2, TotalCount = 80
};
var sample = new[] { 0.0D, 0.0D, 0.0D, 0.0D };
// act
distr.FromSample(sample);
var pars = distr.Params;
// assert
Assert.AreEqual(0.02D, pars.P, EPS);
}
public void MultinomialPartDistribution_Value()
{
// arrange
var distr = new MultinomialPartDistribution();
distr.Params = new MultinomialPartDistribution.Parameters(0.3D);
// act
var v0 = distr.Value(0);
var v1 = distr.Value(1);
var v2 = distr.Value(2);
// assert
Assert.AreEqual(1.0D, v0);
Assert.AreEqual(0.3D, v1);
Assert.AreEqual(0.09D, v2);
}
